Slicer Registration Library Case #12: Liver Tumor Cryoablation

Input

fixed image/target

moving image

Description

We have a pre-operative MRI and an intra-operative CT. We wish to align the MRI to the CT to aid in guiding the surgical intervention. Tumor visibility is much better on the MRI, hence a properly registered MRI can help locate the tumor during the intervention. Approach: we seek a non-rigid registration of the MRI to the CT. Because contrast and image content are very different, we must first build masks to guide the registration process onto the structures of interest. We then obtain the registration in 2 steps: we first compute an affine registration to test stability and feasibility of our masks. Once successful we use this registration as starting point for a BSpline non-rigid transform.

Modules used

Download (from NAMIC MIDAS)

Why 2 sets of files? The "input data" mrb includes only the unregistered data to try the method yourself from start to finish. The full dataset includes intermediate files and results (transforms, resampled images etc.). If you use the full dataset we recommend to choose different names for the images/results you create yourself to distinguish the old data from the new one you generated yourself.

Objective / Background

Keywords

Input Data

reference/fixed : pr-op CT, 0.95 x 0.95 x 5 mm voxel size

moving: intra-op MRI, 0.78 x 0.78 x 2.5 mm axial,

Procedures

Phase I: Build Masks

Note: for illustration the example set contains 2 masks: one with only the liver and one also including spleen and kidney (Mask2). As shown in the results below, the liver-only mask is insufficiently constraining the registration, yielding a result that at first glance looks ok for the liver, but has significant misalignment in the remaining abdominal area. Hence it is advisable to stabilize the registration further by including more structures with good contrast in both images (Spleen, Kidney).